






"""simple docstring"""
import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     AlbertForMaskedLM,
     AlbertForMultipleChoice,
     AlbertForPreTraining,
     AlbertForQuestionAnswering,
     AlbertForSequenceClassification,
     AlbertForTokenClassification,
     AlbertModel,
 )
 from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class        UpperCamelCase__ :







    """simple docstring"""







    def __init__(		self :	List[Any]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	int=1_3				,    UpperCamelCase_ :	Dict=7				,    UpperCamelCase_ :	Optional[Any]=True				,    UpperCamelCase_ :	int=True				,    UpperCamelCase_ :	str=True				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Any=9_9				,    UpperCamelCase_ :	Dict=1_6				,    UpperCamelCase_ :	Optional[int]=3_6				,    UpperCamelCase_ :	List[Any]=6				,    UpperCamelCase_ :	List[Any]=6				,    UpperCamelCase_ :	Any=6				,    UpperCamelCase_ :	List[Any]=3_7				,    UpperCamelCase_ :	Dict="gelu"				,    UpperCamelCase_ :	List[Any]=0.1				,    UpperCamelCase_ :	List[str]=0.1				,    UpperCamelCase_ :	str=5_1_2				,    UpperCamelCase_ :	int=1_6				,    UpperCamelCase_ :	int=2				,    UpperCamelCase_ :	Optional[int]=0.02				,    UpperCamelCase_ :	Tuple=3				,    UpperCamelCase_ :	List[Any]=4				,    UpperCamelCase_ :	List[Any]=None				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    batch_size
           __magic_name__      				=    seq_length
           __magic_name__      				=    is_training
           __magic_name__      				=    use_input_mask
           __magic_name__      				=    use_token_type_ids
           __magic_name__      				=    use_labels
           __magic_name__      				=    vocab_size
           __magic_name__      				=    embedding_size
           __magic_name__      				=    hidden_size
           __magic_name__      				=    num_hidden_layers
           __magic_name__      				=    num_hidden_groups
           __magic_name__      				=    num_attention_heads
           __magic_name__      				=    intermediate_size
           __magic_name__      				=    hidden_act
           __magic_name__      				=    hidden_dropout_prob
           __magic_name__      				=    attention_probs_dropout_prob
           __magic_name__      				=    max_position_embeddings
           __magic_name__      				=    type_vocab_size
           __magic_name__      				=    type_sequence_label_size
           __magic_name__      				=    initializer_range
           __magic_name__      				=    num_labels
           __magic_name__      				=    num_choices
           __magic_name__      				=    scope







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

           __magic_name__      				=    None
           if self.use_input_mask:
                  __magic_name__      				=    random_attention_mask([self.batch_size, self.seq_length] )

           __magic_name__      				=    None
           if self.use_token_type_ids:
                  __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size )

           __magic_name__      				=    None
           __magic_name__      				=    None
           __magic_name__      				=    None
           if self.use_labels:
                  __magic_name__      				=    ids_tensor([self.batch_size]				,    self.type_sequence_label_size )
                  __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels )
                  __magic_name__      				=    ids_tensor([self.batch_size]				,    self.num_choices )

           __magic_name__      				=    self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           return AlbertConfig(
               vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    initializer_range=self.initializer_range				,    num_hidden_groups=self.num_hidden_groups				,    )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    AlbertModel(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_ )
           __magic_name__      				=    model(UpperCamelCase_				,    token_type_ids=UpperCamelCase_ )
           __magic_name__      				=    model(UpperCamelCase_ )
           self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertEqual(result.pooler_output.shape				,    (self.batch_size, self.hidden_size) )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    AlbertForPreTraining(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(
               UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    labels=UpperCamelCase_				,    sentence_order_label=UpperCamelCase_				,    )
           self.parent.assertEqual(result.prediction_logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )
           self.parent.assertEqual(result.sop_logits.shape				,    (self.batch_size, config.num_labels) )







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    AlbertForMaskedLM(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    AlbertForQuestionAnswering(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(
               UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    start_positions=UpperCamelCase_				,    end_positions=UpperCamelCase_				,    )
           self.parent.assertEqual(result.start_logits.shape				,    (self.batch_size, self.seq_length) )
           self.parent.assertEqual(result.end_logits.shape				,    (self.batch_size, self.seq_length) )







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.num_labels
           __magic_name__      				=    AlbertForSequenceClassification(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels) )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.num_labels
           __magic_name__      				=    AlbertForTokenClassification(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels) )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.num_choices
           __magic_name__      				=    AlbertForMultipleChoice(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    input_ids.unsqueeze(1 ).expand(-1				,    self.num_choices				,    -1 ).contiguous()
           __magic_name__      				=    token_type_ids.unsqueeze(1 ).expand(-1				,    self.num_choices				,    -1 ).contiguous()
           __magic_name__      				=    input_mask.unsqueeze(1 ).expand(-1				,    self.num_choices				,    -1 ).contiguous()
           __magic_name__      				=    model(
               UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    token_type_ids=UpperCamelCase_				,    labels=UpperCamelCase_				,    )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices) )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    self.prepare_config_and_inputs()
           (
               (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  (
               __magic_name__
           )				,  
           )      				=    config_and_inputs
           __magic_name__      				=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict



@require_torch
class        UpperCamelCase__ (				a_							,	a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = (
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    __UpperCAmelCase           = (
        {
            """feature-extraction""": AlbertModel,
            """fill-mask""": AlbertForMaskedLM,
            """question-answering""": AlbertForQuestionAnswering,
            """text-classification""": AlbertForSequenceClassification,
            """token-classification""": AlbertForTokenClassification,
            """zero-shot""": AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCAmelCase           = True







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Optional[Any]=False ):





           '''simple docstring'''

           __magic_name__      				=    super()._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_				,    return_labels=UpperCamelCase_ )

           if return_labels:
                  if model_class in get_values(UpperCamelCase_ ):
                         __magic_name__      				=    torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.seq_length)				,    dtype=torch.long				,    device=UpperCamelCase_ )
                         __magic_name__      				=    torch.zeros(
                             self.model_tester.batch_size				,    dtype=torch.long				,    device=UpperCamelCase_ )
           return inputs_dict







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    AlbertModelTester(self )
           __magic_name__      				=    ConfigTester(self				,    config_class=UpperCamelCase_				,    hidden_size=3_7 )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_ )







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_ )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_ )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_ )







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __magic_name__      				=    type
                  self.model_tester.create_and_check_model(*UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __magic_name__      				=    AlbertModel.from_pretrained(UpperCamelCase_ )
                  self.assertIsNotNone(UpperCamelCase_ )



@require_torch
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @slow
    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    AlbertModel.from_pretrained('albert-base-v2' )
           __magic_name__      				=    torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
           __magic_name__      				=    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
           with torch.no_grad():
                  __magic_name__      				=    model(UpperCamelCase_				,    attention_mask=UpperCamelCase_ )[0]
           __magic_name__      				=    torch.Size((1, 1_1, 7_6_8) )
           self.assertEqual(output.shape				,    UpperCamelCase_ )
           __magic_name__      				=    torch.tensor(
               [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,    UpperCamelCase_				,    atol=1e-4 ) )







"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)